[BugFix][310P][v0.18.0] Use CPU generator cache for sampling (#8624)

### What this PR does / why we need it?
This PR introduces a caching mechanism for CPU-based `torch.Generator`
objects in the `_random_sample_310p` function to optimize sampling
performance. It includes unit tests for cache persistence and state
recovery. Feedback highlights a critical bug where keying the cache by
batch index instead of generator ID can break RNG reproducibility during
request re-scheduling, and notes a potential memory leak in the global
cache.

### Does this PR introduce _any_ user-facing change?
No.

### How was this patch tested?
Tested via new unit tests in `tests/ut/_310p/sample/test_sampler_310.py`
verifying cache logic and error handling.

---------

Signed-off-by: csoulnd <daidaicurry@foxmail.com>

vllm_ascend/_310p/sample/sampler.py

@@ -25,6 +25,8 @@ from vllm_ascend.sample.sampler import (
 )
 from vllm_ascend.utils import global_stream, npu_stream_switch
 
+_CPU_GENERATOR_CACHE_310P: dict[int, tuple[torch.Generator, int]] = {}
+
 
 def _random_sample_310p(
     probs: torch.Tensor,
@@ -38,7 +40,18 @@ def _random_sample_310p(
             q.exponential_()
         if generators:
             for i, generator in generators.items():
-                q[i].exponential_(generator=generator)
+                cache_entry = _CPU_GENERATOR_CACHE_310P.get(i)
+                if cache_entry is None or cache_entry[1] != id(generator):
+                    cpu_generator = torch.Generator(device="cpu")
+                    try:
+                        # Keep RNG stream consistent with the original generator.
+                        cpu_generator.set_state(generator.get_state())
+                    except Exception:
+                        cpu_generator.manual_seed(generator.initial_seed())
+                    cache_entry = (cpu_generator, id(generator))
+                    _CPU_GENERATOR_CACHE_310P[i] = cache_entry
+                cpu_generator, _ = cache_entry
+                q[i].exponential_(generator=cpu_generator)
         q = q.npu()
     torch.npu.current_stream().wait_stream(global_stream())
     return probs.div_(q).argmax(dim=-1).view(-1)